rtma_cdf

Compute theoretical and empirical CDFs for a right-truncated meta-analysis

Fits right-truncated meta-analysis (RTMA), a bias correction for
the joint effects of p-hacking (i.e., manipulation of results within studies
to obtain significant, positive estimates) and traditional publication bias
(i.e., the selective publication of studies with significant, positive
results) in meta-analyses [see Mathur MB (2022). "Sensitivity analysis for
p-hacking in meta-analyses." <doi:10.31219/osf.io/ezjsx>.]. Unlike publication
bias alone, p-hacking that favors significant, positive results (termed
"affirmative") can distort the distribution of affirmative results. To
bias-correct results from affirmative studies would require strong assumptions
on the exact nature of p-hacking. In contrast, joint p-hacking and publication
bias do not distort the distribution of published nonaffirmative results when
there is stringent p-hacking (e.g., investigators who hack always eventually
obtain an affirmative result) or when there is stringent publication bias
(e.g., nonaffirmative results from hacked studies are never published). This
means that any published nonaffirmative results are from unhacked studies.
Under these assumptions, RTMA involves analyzing only the published
nonaffirmative results to essentially impute the full underlying distribution
of all results prior to selection due to p-hacking and/or publication bias.
The package also provides diagnostic plots described in Mathur (2022).

Peter Solymos

phacking

Sensitivity Analysis for p-Hacking in Meta-Analyses

Maya Mathur

Mika Braginsky

rtma_cdf function

<dl><dt>rtma</dt>
<dd>Output of <code>phacking_meta()</code>.</dd></dl>

Arguments

Compute theoretical and empirical CDFs for a right-truncated meta-analysis — rtma_cdf

<dl>

<dt>rtma</dt>
<dd>Output of <code>phacking_meta()</code>.</dd>

</dl>

rtma_cdf: Compute theoretical and empirical CDFs for a right-truncated meta-analysis

Description

Usage

Value

Arguments

References

Examples